Analytics Quinnipiac

Quinnipiac Assignment #04 – Media Convergence

Quinnipiac Assignment #04 – Media Convergence

Once again, we did not have to prepare a YouTube video. Therefore, instead, I am going to reprint one of my essays, in its entirety. This one is about media convergence. As media (print, television, Internet, etc.) all becomes deliverable on one piece of hardware (generally a smartphone or an iPad), and one is advertised or copied or shared on another, should our metrics and means of measuring reach, etc. on these platforms also diverge? And what does that mean for the future of measurement?

Media Convergence

Wishing and Hoping AKA The Past

Back even before television was three channels, data was gathered via Nielsen ratings. Nielsen started in the 1920s but didn’t really get into media analysis until the 1942 radio index. In 1950, it was followed by the television index. (Nielsen, 90 Years, By 2000, Nielsen had gotten into measuring Internet usage.

Throughout most of this nine-decade period, media was siloed. Radio was analyzed one way, television another, etc. But it was mainly counting. How many people watched a show? How many listened to a particular radio station? With 1987’s People Meter (Nielsen, 25 Years of the People Meter,, an effort was made to gather more granular data, and to gather it more rapidly. However, Nielsen’s efforts were still confined to extrapolated samples. Was their sampling correct? In 1992, the People Meter was used for the first time in an attempt to measure Hispanic viewing habits. But even in 2012, the total number of people meters in use was in a mere 20,000 households. Were the samples representative? It’s hard to say.

Here and Now AKA It’s Better, But ….

Social media qualitative measurements, including sentiment analysis, are an effort to understand viewer, user, and listener behaviors. Nielsen and the like measure quantifiable information such as time on a channel (or page). But qualitative measurement goes beyond that, in an effort to understand why people visit a website. Topsy, for example, measures the number of positive and negative mentions of a site, product, service, celebrity, etc. Yet a lot of this is still quantitative data. Consider Martha Stewart as a topic of online conversation.

Adventures in Career Changing | Janet Gershen-Siegel | Quinnipiac Assignment 04 – ICM 524 Media Convergence
Martha Stewart on Topsy, June 9, 2014


All we can see are numbers, really (the spike was on the day that a tweet emerged claiming that Martha Stewart had a drone). This is still counting. There are no insights into why that tweet resonated more than others.

Media convergence is mashing everything together in ways that audiences probably didn’t think were possible even a scant thirty years ago. But now, we watch our television shows online, we are encouraged to tweet to our favorite radio stations, our YouTube videos become part of television advertising, and our Tumblr images are being slipped into online newspapers. All of this and more can be seen on our iPads. Add a phone to this (or just use an iPhone or an Android phone instead of an iPad), and you’ve got nearly everything bundled together. How is this changing analytics? For one thing, what is it that we are measuring? When we see a music video on YouTube, are we measuring viewer sentiments about the sounds or the images? When we measure a television program’s Facebook engagement, is it directly related to the programming, to the channel, to viewer sentiment about the actors or the writers, or something else? What does it mean to like or +one anything anymore, when a lot of people seem to reflexively vote up their friends’ shared content?

I believe that our analysis has got to converge as our media and our devices converge. After all, what is the online experience these days? On any given day, a person might use their iPad to look up a restaurant on Yelp, get directions on Google Maps, view the menu on the restaurant’s own website, check in via FourSquare, take a picture of their plate and upload it to Instagram, and even share their dining experience via a Facebook photo album, a short Vine video or a few quick tweets. If the restaurant gets some of that person’s friends as new customers, where did they come from? The review on Yelp? The check in via FourSquare? The Vine video? The Facebook album? The tweets? The Instagram image? Or was it some combination thereof?

Avinash Kaushik talks about multitouch campaign attribution analysis (Avinash Kaushik, Web Analytics 2.0, Pages 358 – 368), whereby customers might receive messages about a site, product, service, etc. from any number of different sources. On Page 358, he writes, “During the visits leading up to the conversion, the customer was likely exposed to many advertisements from your company, such as a banner ad or Affiliate promotion. Or the customer may have been contacted via marketing promotions, such as an email campaign. In the industry, each exposure is considered a touch by the company. If a customer is touched multiple times before converting, you get a multitouch conversion.” Kaushik reveals that measuring which message caused a conversion is an extraordinarily difficult thing to do.

With media convergence, the number of touches in a campaign can begin to come together. Facebook likes can be measured for all channels. Tweets can be counted for whichever messages are being sent on Twitter, whatever they are about. Will attribution be any easier? Hard to say, but if the number of channels continues to collapse into one, will it matter quite so much in the future?

Analytics Quinnipiac

Quinnipiac Assignment #03 – Social Media Monitoring Tools

Social Media Monitoring Tools

This week, I wrote an essay and recorded a video about various social media monitoring tools, in the context of watching the NESN (New England Sports Network) website.

My essay is reprinted here in its entirety, except for the graphics.

Social Media Brand Report – NESN – Module 03 ICM 524

Company Brand

New England Sports Network (NESN) is a regional television channel with a wide and varied social mediapresence. The industry is cable (Xfinity) media; their home page is:, which is a WordPress blog.

The television channel’s coverage is of nationally-televised (yet regionally-based) sports such as Red Sox baseball, and local events such as North Attleboro, Massachusetts track and field. There are related stories, about sports but outside the region, which round out the coverage.

The specific campaign is the coverage of area sports on various social media outlets.

Brand Sentiment

According to Topsy, brand sentiment is generally mixed but leans toward positive. The sentiment score for the past month (after a survey of over 9,500 mentions) is 54/100. Social Mention more or less confirms these findings, although that service found more positive than negative mentionings, and turned in a far more impressive 7:1 ratio of positive to negative comments. However, just like on Topsy, the majority of mentionings were found to be neutral ones.

Retweet Rank showed that NESN garners a lot of retweets. They’re in the 98th percentile. This is probably to be expected, with over 100,000 Twitter followers.

Social Campaign Initiative

The brand’s social campaign appears to be not only to promote NESN and increase its viewership, but it may also be to increase viewership on other Xfinity properties.  Further, there seem to be attempts to get viewers to click on advertisements for an M-rated video game (Murdered).

Standard tweets are partly to spread NESN’s own content, but also to spread content about related persons, such as players and potential players for the New England Patriots and the Boston Red Sox. A typical tweet (on June 3, 2014) is as follows:

NESN1:36pm via Twitter Web Client These #Patriots veterans are on the Which one(s) won’t make the Week 1 roster?

Their Facebook campaign appears to be nearly (if not completely) 100% fed by their own site’s home page.

NESN Facebook page June 3, 2014
NESN Facebook page June 3, 2014

The same story about new soccer uniforms appears on the NESN home page, in their Twitter stream, and on their Facebook page.

Quantitative Data

NESN does not hide its quantitative data, providing a link at: page provides links to a number of sites which collect quantitative data.

Chartbeat presented a number of vanity metrics, and provided a comparison to the previous day’s metrics.

Topsy was not linked to on NESN, but their take on the site is still of interest.

However, Topsy’s compilations of mentionings are perhaps too inclusive. It includes some misapplied data. For example, the following tweet shows up in Topsy’s report, even though it has nothing to do with NESN, New England, or sports:

Adams @buyharddrive #7: WD My Passport Pro 2TB portable RAID storage with integrated Thunderbolt cable (WDBRMP0020DBK-NESN): WD My…

Qualitative Data

Qualitative data can, in a way, be cobbled together from some of the above-mentioned metrics.

Social Mention’s compilations of mentionings and then parsing them out by positive, negative, and neutral sentiments can provide some qualitative information about how the brand is perceived online. However, some of the mentionings are likely being misfiled.

Their list of negative mentions is a mystery. Perhaps the term ‘pitch’ is being listed as a negative? Because otherwise I cannot see why the following from Reddit is negative at all:

GAME THREAD: Boston Red Sox (27-29) @ Cleveland Indians (27-30) – June 02, 2014 Boston Red Sox @ Cleveland Indians First Pitch Media Feed Channel Subreddits 06:05 PM CT Video CLE WKYC 3, SportsTime Ohio /r/WahoosTipi Weather BOS NESN /r/RedSox P…

Admittedly, this kind of parsing of mentionings is inexact at the best of times, for the software likely does not have a sarcasm detector, either. And, just like on Topsy, anything with NESN in it was fair game for the software, even though the first hit is for the sale of a hard drive that just so happens to have that particular combination of letters in its serial number.

Brand Reputation on Social Media

The brand’s reputation seems to be best outlined by Social Mention, and it appears to be overwhelmingly positive. This can be seen by an inspection of the quantitative retweet metric (very high) and Social Mention’s own qualitative measurements. Even Topsy’s overly inclusive listings don’t seem to tip the scales into the negative. It might not be beloved, but NESN is certainly well-liked on social media. Even negative stories don’t seem to hurt its reputation, as its followers on social media seem savvy enough to not want to shoot the messenger if their beloved teams lose.

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Analytics Quinnipiac

Quinnipiac Assignment #02 – Qualitative and Quantitative Analytics

Quinnipiac Assignment #02 – Qualitative and Quantitative Analytics

This week’s assignments at Quinnipiac were centered around the differences between quantitative and qualitative analytics. I had a couple of essays to write.

I have decided to reprint one of my essays here, in its entirety.

Qualitative versus Quantitative

I think that a choice between the two is, perhaps, misplaced.

English: Interactive Visualization of Qualitat...
English: Interactive Visualization of Qualitative and Quantitative data in a web based mixed methods application (Photo credit: Wikipedia)

Aren’t both of these necessary, in order to really see the big picture?

We Love Quantitative Data

Probably the best part of quantitative data is that it’s relatively easy to obtain, particularly online. Consider this – do we, given the current state of technology, know everyone who comes into, say, a department store?

Web analytics framework
Web analytics framework (Photo credit: Beantin webbkommunikation)

Even when we break this down to hourly increments, and even if we look at closed-circuit cameras, we still might miss someone. After all, if a person leaves and comes back later, we might not notice that it’s the same person.

Not so with the web. Cookies and other tracking codes give us the ability to know that a device has returned; and an account if our site allows for user accounts. That still doesn’t help us if everyone in a household uses the same account, but it’s a start.

We look at our web data and we think – aha! User #12345 has returned four times in one day!

And then we have no idea why that happened, and no way to capitalize on it. It’s the ultimate in vanity metrics, e. g. it’s stuff that can be measured but it isn’t necessarily actionable, or even desirable information.

We Love Qualitative Data

With qualitative data, we get more into the whys and wherefores.

coding cat-egories
coding cat-egories (Photo credit: urbanmkr)

Why did User #12345 return four times in one day? If a purchase is made on the fourth go-‘round, that’s terrific. But why were there three other visits? Even someone performing research and then returning later might not necessarily visit two more times. What’s up with that?

Maybe the website was slow those two other times. Maybe User #12345 got busy and abandoned the cart for Visit #2 and Visit #3. Some of this is inferential. Some of it can be proven, such as site slowness or at least traffic spikes that could imply speed issues. We can’t get into User #12345’s head (at least, not yet).

We REALLY Love Them Together

I think we’ve got to look at the two types together.

In the Huffington Post article, The Big Data Craze Is Just as Qualitative as It Is Quantitative?, Sean Donahue writes, “But for brands, political campaigns and advocacy organizations that aim to have data-driven conversations with audiences, it will be more important than ever to apply qualitative logic and human reasoning to online analytical models. In short, subject matter expertise and deep knowledge will matter more than ever before given the rise of big data.

As communicators, even with what we have at our fingertips today, we need to immerse ourselves in the substance that contextualizes big data and allows us to make sense out of it. This means committing more time, asking more questions, consuming more content and never losing sight of the fact that data without actionable insights is meaningless.”

I believe that what Donahue is saying is that we can and will be getting great big garbage bags full of data, and soon even more of it will be at low or no cost. But without contextual analysis, it’s somewhat meaningless.

Further to that is Anmol Rajpurohit’s point in Qualitative Analytics: Why numbers do not tell the complete story?, wherein he writes, “Quantitative analytics still needs more manual intervention and the results are often fuzzy. In absence of a clear-cut approach and thus automation, it is not as time and energy efficient as the traditional quantitative analytics. But, qualitative analytics is still indispensable as it provides deep, actionable insights about the ‘why’ and ‘how’ aspect, which often gets ignored as we continue to be inundated with the ‘what’ ‘where’ and ‘when’ of statistics.”

As Rajpurohit indicates, qualitative data is fuzzy and manual and not automated. It’s a slow process (and perhaps a less exact science than quantitative), yet it remains necessary to a holistic understanding of online data.

06/30: It's Peanut Butter Jelly Time!!!
06/30: It’s Peanut Butter Jelly Time!!! (Photo credit: ttyS0)

To me, this is peanut butter and jelly. They’re fine separate, but they work best together. With purely quantitative data, we can know that a particular literary passage has 278 words. We can know that two of its longest words are twelve letters long: consummation and undiscovered. We can find that the (probably) most frequently-used word is ‘the’, with twenty occurrences. With qualitative analysis, we learn that the mystery passage is Hamlet’s To Be or Not to Be soliloquy. With qualitative analysis, it stops being a laundry list of words, and its context affords a meaning that goes beyond bare statistics.


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Analytics Quinnipiac

Quinnipiac Assignment 01 – Qualitative and Quantitative Analytics in my Life

Quinnipiac Assignment 01 – Qualitative and Quantitative Analytics in my Life

Quinnipiac University
Quinnipiac University (Photo credit: Wikipedia)

I began a new semester at Quinnipiac University; this new course is on social media analytics, which includes Google Analytics plus the collecting and interpreting of actionable data.

My professor is Eleanor Hong, who was also my professor for Social Media Platforms. I had really loved that class, so I made sure to take this one with her as well.

Our first assignment was to create a video. I was very pleased to see some names that I knew who are taking the course with me and I had originally met in Social Media Platforms. My final project partner from that course, though (Kim Scroggins), is graduating later this year and is instead just taking a Master’s Degree capstone project credit course. I have to admit that I do miss my final project partner a bit!

It already looks like it will be an interesting course. This video is about quantitative and qualitative analytics that I use in my daily life.

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